def test_enforce_connectivity():
img = np.array(
[[0, 0, 0, 1, 1, 1], [1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 0]], np.float)
segments_connected = slic(
img,
2,
compactness=0.0001,
enforce_connectivity=True,
convert2lab=False,
)
# Make sure nothing fatal occurs (e.g. buffer overflow) at low values of
# max_size_factor
segments_connected_low_max = slic(
img,
2,
compactness=0.0001,
enforce_connectivity=True,
convert2lab=False,
max_size_factor=0.8,
)
result_connected = np.array(
[[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], np.float)
assert_equal(segments_connected, result_connected)
assert_equal(segments_connected_low_max, result_connected)
def cuda_slic():
labels = slic(img_lab,
n_segments=100,
compactness=5,
max_iter=5,
convert2lab=False,
enforce_connectivity=False)
return labels
def test_spacing():
rnd = np.random.RandomState(0)
img = np.array([[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], np.float)
result_non_spaced = np.array([[0, 0, 0, 1, 1], [0, 0, 1, 1, 1]], np.int)
result_spaced = np.array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]], np.int)
img += 0.1 * rnd.normal(size=img.shape)
seg_non_spaced = slic(img,
n_segments=2,
multichannel=False,
compactness=1.0)
seg_spaced = slic(
img,
n_segments=2,
spacing=[1, 500, 1],
compactness=1.0,
multichannel=False,
)
assert_equal(seg_non_spaced, result_non_spaced)
assert_equal(seg_spaced, result_spaced)
def test_more_segments_than_pixels():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21))
img[:10, :10] = 0.33
img[10:, :10] = 0.67
img[10:, 10:] = 1.00
img += 0.0033 * rnd.normal(size=img.shape)
img[img > 1] = 1
img[img < 0] = 0
seg = slic(
img,
n_segments=500,
compactness=1,
multichannel=False,
convert2lab=False,
)
assert np.all(seg.ravel() == np.arange(seg.size))
def test_color_3d():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21, 22, 3))
slices = []
for dim_size in img.shape[:-1]:
midpoint = dim_size // 2
slices.append((slice(None, midpoint), slice(midpoint, None)))
slices = list(product(*slices))
colors = list(product(*(([0, 1], ) * 3)))
for s, c in zip(slices, colors):
img[s] = c
img += 0.01 * rnd.normal(size=img.shape)
img[img > 1] = 1
img[img < 0] = 0
seg = slic(img, n_segments=8)
assert_equal(len(np.unique(seg)), 8)
for s, c in zip(slices, range(8)):
assert_equal(seg[s], c)
def test_multichannel_2d():
rnd = np.random.RandomState(0)
img = np.zeros((20, 20, 8))
img[:10, :10, 0:2] = 1
img[:10, 10:, 2:4] = 1
img[10:, :10, 4:6] = 1
img[10:, 10:, 6:8] = 1
img += 0.01 * rnd.normal(size=img.shape)
img = np.clip(img, 0, 1, out=img)
seg = slic(img, n_segments=4, enforce_connectivity=False)
seg = seg - 1
# we expect 4 segments
assert_equal(len(np.unique(seg)), 4)
assert_equal(seg.shape, img.shape[:-1])
assert_equal(seg[:10, :10], 0)
assert_equal(seg[10:, :10], 2)
assert_equal(seg[:10, 10:], 1)
assert_equal(seg[10:, 10:], 3)
def test_color_2d():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21, 3))
img[:10, :10, 0] = 1
img[10:, :10, 1] = 1
img[10:, 10:, 2] = 1
img += 0.01 * rnd.normal(size=img.shape)
img[img > 1] = 1
img[img < 0] = 0
seg = slic(img, n_segments=4, enforce_connectivity=False)
seg = seg - 1
# we expect 4 segments
assert_equal(len(np.unique(seg)), 4)
assert_equal(seg.shape, img.shape[:-1])
assert_equal(seg[:10, :10], 0)
assert_equal(seg[10:, :10], 2)
assert_equal(seg[:10, 10:], 1)
assert_equal(seg[10:, 10:], 3)
def test_gray_2d():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21))
img[:10, :10] = 0.33
img[10:, :10] = 0.67
img[10:, 10:] = 1.00
img += 0.0033 * rnd.normal(size=img.shape)
img[img > 1] = 1
img[img < 0] = 0
seg = slic(img,
n_segments=4,
compactness=1,
multichannel=False,
convert2lab=False)
assert_equal(len(np.unique(seg)), 4)
assert_equal(seg.shape, img.shape)
assert_equal(seg[:10, :10], 0)
assert_equal(seg[10:, :10], 2)
assert_equal(seg[:10, 10:], 1)
assert_equal(seg[10:, 10:], 3)
def test_gray_3d():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21, 22))
slices = []
for dim_size in img.shape:
midpoint = dim_size // 2
slices.append((slice(None, midpoint), slice(midpoint, None)))
slices = list(product(*slices))
shades = np.arange(0, 1.000001, 1.0 / 7)
for s, sh in zip(slices, shades):
img[s] = sh
img += 0.001 * rnd.normal(size=img.shape)
img[img > 1] = 1
img[img < 0] = 0
seg = slic(img,
n_segments=8,
compactness=1,
multichannel=False,
convert2lab=False)
assert_equal(len(np.unique(seg)), 8)
for s, c in zip(slices, range(8)):
assert_equal(seg[s], c)
def cuda_slic(img_lab, n_segments, compactness, max_iter=3):
labels = slic(img_lab, n_segments=n_segments, compactness=compactness, max_iter=max_iter, convert2lab=False,
enforce_connectivity=False)
return labels
def test_dtype_support(dtype):
img = np.random.rand(28, 28).astype(dtype)
# Simply run the function to assert that it runs without error
slic(img)
def test_invalid_lab_conversion():
img = np.array([[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], np.float) + 1
with testing.raises(ValueError):
slic(img, multichannel=True, convert2lab=True)
